Spatial patterns in host-associated and free-living bacterial communities across six temperate estuaries

Abstract A major goal of microbial ecology is to establish the importance of spatial and environmental factors in driving community variation. Their relative importance likely varies across spatial scales, but focus has primarily been on free-living communities within well-connected aquatic environments rather than less connected island-like habitats such as estuaries, and key host-associated communities within these systems. Here we sampled both free-living (seawater and sediment) and host-associated (estuarine fish hindgut microbiome, Pelates sexlineatus) communities across six temperate Australian estuaries spanning ∼500 km. We find that spatial and environmental factors have different influences on these communities, with seawater demonstrating strong distance-decay relationships (R = −0.69) and significant associations with a range of environmental variables. Distance-decay relationships were weak for sediment communities but became stronger over smaller spatial scales (within estuaries, R = −0.5), potentially reflecting environmental filtering across biogeochemical gradients or stochastic processes within estuary sediments. Finally, P. sexlineatus hindgut microbiome communities displayed weak distance-decay relationships (R = −0.36), and limited variation explained by environmental variables, indicating the significance of host-related factors in driving community variation. Our findings provide important ecological insights into the spatial distributions and driving forces of both free-living and host-associated bacterial patterns across temperate estuarine systems.


Introduction
Micr oor ganisms play crucial roles in global biogeochemical cycles, particularly critical to the functioning of dynamic environments such as estuaries (Hanson et al. 2012, Zhang et al. 2022. A k e y focus of marine microbial ecology is the study of spatial patterns and related environmental factors in order to understand the mec hanisms r esponsible for the gener ation and maintenance of diversity (Van der Gucht et al. 2007, Hanson et al. 2012 ). The contributions of spatial factors and local environmental conditions ar e consider ed to be major competitiv e forces in driving micr obial biogeogr a phy; ho w e v er, these ar e thought to be lar gel y scaledependent: on a global scale, spatial separation tends to overwhelm local environmental effects; at small scales, environmental effects are the major determinants; and at intermediate scales, both local environmental conditions and spatial factors are important drivers of community variation (Martiny et al. 2006(Martiny et al. , 2011. The Baas-Becking hypothesis "everything is everywhere, but the envir onment selects" (Baas-Bec king 1934 ) implies that all micr oorganisms ar e globall y distributed, with local envir onmental conditions driving selection of distinctive assemblages (De Wit and Bouvier 2006 ). This early hypothesis has been heavily debated by numer ous studies, whic h hav e demonstr ated geogr a phic separ ation independent of changes in the environment, indicating dispersal limitation (Langenheder and Ragnarsson 2007 ). One of the most commonly studied biogeographic patterns is the distancedecay r elationship, whic h r efers to decr easing comm unity similar-ity with increasing geographic distance (Hanson et al. 2012, Zhang et al. 2022 ). Similar to patterns observ ed for macr oor ganisms, distance-decay relationships are thought to be stronger in heterogeneous habitats and for island-like habitats compared to continuous environments (Prosser et al. 2007, Zinger et al. 2014 ); howe v er, patterns ar e influenced by both deterministic and stochastic processes (Wang et al. 2013 ), highlighting the importance of identifying major assembl y pr ocesses gov erning comm unity div ersity.
In order to increase our understanding of the contributions of spatial and local environmental effects on microbial communities, it is also crucial to consider different bacterial habitats (e.g. free-living vs host-associated) (Taylor et al. 2005 ). While distributions of free-living microorganisms may be strongly driven by dispersal limitation and environmental filtering (Fuhrman et al. 2008, Wietz et al. 2010, Zhang et al. 2022, host or ganisms pr o vide unique en vironmental conditions that differ from those in the surrounding seawater or sediments and may ther efor e function as island habitats, allowing for allopatric speciation of symbiotic microbes living in physically separated hosts, resulting in distinct host-associated communities and contributing to distance-decay patterns (P a pke and Ward , Taylor et al. 2005 ). Dispersal of host-associated communities is therefore likely dependent on the movement or migration of the host, with these comm unities compar ativ el y mor e buffer ed fr om the effects of envir onmental conditions (Dic k e y et al. 2021 ). Despite this, the biogeogr a phy of host genotypes and local environmental-genotype inter actions hav e been documented, along with environmental filtering through diet in shaping associated microbiomes (Spor et al. 2011, Wagner et al. 2016, Loo et al. 2019, Baltrus 2020, Baldassarre et al. 2022. Evidence indicates that both host-related and environmental factors drive variation in microbial communities associated with k e y marine hosts such as sponges and corals (Taylor et al. 2005, Luter et al. 2015, Rubio-Portillo et al. 2018, Easson et al. 2020; ho w e v er, knowledge of the r elativ e importance of these remains limited, especially in important estuarine host organisms such as fish.
Within coastal systems, fish hav e gr eat ecological, economic, and cultural significance (Schlacher et al. 2005 ), representing an important study organism for addressing questions for hostmicrobe associations and microbial diversity. Despite their significance and the recognition that fish gut microbiome interactions are important for host fitness, metabolism, and immunity, host-micr obiome inter actions in fish ar e understudied in comparison to other v ertebr ates (Ghanbari et al. 2015 , Colston andJackson 2016 ). Fish are constantly exposed to the surrounding seawater and sediments via dietary uptake; ho w e v er, ther e is evidence that gut microbial communities are distinct from those in the surr ounding envir onment (Nav arr ete et al. 2012, Li et al. 2015, suggesting water-borne dispersal between hosts may be negligible. While such spatial patterns have been established in free-living micr obial comm unities (Zinger et al. 2014, Wang et al. 2015, Zhang et al. 2022, investigations into these patterns hav e been lar gel y ov erlooked for hosts occupying dynamic and complex estuarine systems such as fish. Both environmental and spatial variability are important driv ers of micr obial v ariation in continuous aquatic environments, but these patterns are less clear in less connected, islandlike habitats (Hanson et al. 2012 ), such as estuaries. Estuaries represent unique hotspots of biogeochemical cycles with high microbial biodiversity supported along biogeochemical gradients (Webster et al. 2015, Zhang et al. 2022. Here, we sampled both free-living and host-associated micr obial comm unities across an intermediate scale of ∼500 km, spanning six east Australian estuaries, and aimed to address the following questions: what influence do spatial and environmental factors have on micr obial comm unities, and does this differ for fr ee-living vs hostassociated communities? We hypothesized that: (a) distancedecay relationships would be stronger for free-living than hostassociated communities due to limited movement of environmental bacteria between estuaries; (b) at the intermediate scale sampled in this study, spatial effects and local environmental variation would both contribute to spatial patterns across estuaries; and (c) free-living communities would be more strongly shaped by local environmental variation than host-associated communities due to their closer association with the environment.

Field
This r esearc h was conducted under the Univ ersity of Ne wcastle Animal Ethics Protocol A-2020-026. Six estuaries along the NSW coastline were selected for sampling, based on available seagrass habitat and distribution of the estuarine fish Pelates sexlineatus (eastern striped grunter): Hastings Ri ver, Wallis Lak e, Lak e Macquarie, Brisbane Water, Lake Illawarra, and Burrill Lake (Fig. 1 ). Pelates sexlineatus is a common estuarine fish selected for its broad distribution in sea gr ass meadows along the south-east coast of New South Wales (Pollard 1984, Trnski and Neira 1998, Smith and Suthers 2000. Sea water, sediments , and P. sexlineatus individuals were collected from three sites within each estuary between October and November 2020. The following water quality parameters wer e measur ed at eac h site using a Horiba U-50 water quality meter: temper atur e (pr ecision of 0.01 • C), salinity (0.1 ppt), pH (0.01), turbidity (0.1 NTU), and dissolved oxygen (0.01 mg/L). Seawater was collected in sterile bottles (rinsed with 10% bleach solution) for amplicon sequencing ( n = 5), with ∼800 ml from each sample filtered onto 0.2 μm pore-sized Sterivex filters, depending on the amount of particulate organic matter present. Sediment samples (50-60 ml) were collected using a 50 ml Luer Lock syringe plunged v erticall y into the sediment to ∼100 mm depth, for sediment granulometry and the determination of organic matter . Surface sediments were also collected for amplicon sequencing ( n = 5) by scr a ping the upper 1 cm layer into sterile 15 ml tubes. Pelates sexlineatus were collected using a 10-m seine net pulled through sea gr ass beds for amplicon sequencing of the hindgut microbiome ( n = 5). All samples were immediately stored on ice and processed within 6 h of collection.

DN A extr action and 16S rRN A gene sequencing
Pelates sexlineatus total length (mm) and weight (g) were recorded, and fish hindgut contents r emov ed ( ∼0.25 g). A 0.25 g sample from collected sediments were stored at −80 • C until DNA extr action. Qia gen DNeasy Po w erSoil Kits w er e used to extr act DNA from fish and sediment samples, and Qiagen DNeasy Po w erWater Kits were used for seawater samples. Extraction blanks containing no sample were processed with each batch to serve as controls. Sample quantity and quality were checked using a NanoPhotometer NP80. Bacterial communities were characterized using 16S rDNA gene amplicon sequencing. The V1-V3 region of the 16S rDNA gene fr om pr okaryotes was amplified using universal primers 27F (A GA GTTTGATCMTGGCTCA G) and 519R (GWA TT AC-CGCGGCKGCTG) attached with Illumina adaptors in PCR with the follo wing c ycling conditions: 95 • C for 10 min, then 35 cycles of: 94 • C for 30 s, 55 • C for 10 s, and 72 • C for 45 s, with a final extension of 72 • C for 10 min. PCR products were sequenced using the Illumina Miseq v3 (2 × 300 bp) platform at the Ramaciotti Centre for Genomics at the University of New South Wales. Resultant amplicons wer e pr ocessed using the R pipeline D AD A2 with default parameters (Callahan et al. 2016 ), including the removal of potential c himer as. Reads wer e cluster ed to pr oduce amplicon sequence variants (ASVs) and aligned to the SILVA v132 database (Yilmaz et al. 2014 ) for taxonomic assignment. The dataset was further cleaned by removing singletons and those identified as non-bacterial or originating fr om c hlor oplasts. Of the 90 samples that were collected for each sample type, 65 fish hindgut samples wer e successfull y sequenced, as well as 75 sediment samples, and 50 seawater samples (Supplementary Table S1). Cleaned data wer e r ar efied to 4276 r eads per sample (see Supplementary Figure S1 for rarefaction curves on raw and rarefied data, and Supplementary Table S1 for a summary of samples sequenced and retained after rarefaction). Samples with reads below this threshold (se v en fish hindgut samples and tw o seaw ater samples) w ere r emov ed in order to retain diversity after rarefaction.

Sta tistical anal ysis
Statistical analysis was carried out in RStudio using the "vegan" pac ka ge (R Cor e Team 2018 , Oksanen et al. 2019 ). Micr obial community data was analyzed using the "phyloseq" package (McMurdie and Holmes 2013 ), with each sample type (seawater, sediment, and fish hindgut) analyzed se parately. Di versity metrics were cal- culated using r ar efied sequence data. Linear r egr ession models w ere emplo y ed to test correlations betw een bacterial community similarity (Bray-Curtis similarity) and geographic distance (km 2 ) and envir onmental v ariables (Euclidean distances), and distancedecay slopes were visualized using scatterplots. PERMANOVA with pairwise comparisons was used to investigate significant differences in community composition (based on Bray-Curtis dissimilarity) between estuaries, and PERMDISP was further employed to inv estigate differ ences in comm unity v ariance acr oss estuaries using the "betadisper" function (Oksanen et al. 2019 ). ANOVA with Tuk e y's HSD was employed to assess significant differences in alpha diversity (observed richness and Shannon diversity). T he following en vir onmental v ariables wer e extr acted fr om the NSW Estuary Health Monitoring, Evaluating, and Reporting (MER) pr ogr am on the SEED NSW database ([dataset] * Department of Planning and Environment 2020 ) for each of the sampled estuaries: av er a ge depth (m), flushing time (days), catc hment ar ea (km 2 ), catc hment clear ed (%), urbanization (%), estuary surface area (km 2 ), and estuary volume (ML). NSW estuarine macrophyte data was obtained from the Fisheries NSW Spatial Data Portal ([dataset] * NSW Department of Primary Industries), and sea gr ass, mangr ov e, and saltmarsh ar ea (km 2 ) for eac h estuary was calculated in QGIS 3 (QGIS.org 2022 et al. 2022 ) using the GroupStats plugin (HenrikSpa 2021 ). Envir onmental v ariables used in the study ar e pr esented in Supplementary Table S2 and w ere sho wn to change significantly over the sampling area (with geographic distance) (see Supplementary Table S3). Environmental variables wer e c hec ked for collinearity, and those that were highly correlated with other variables ( R ≥ 0.8) were removed from further statistical analyses. Mantel and partial mantel tests were used to test the correlation between environmental variability and geogr a phic distance on variation of bacterial alpha diversity between sites (Euclidean distances). Redundancy analysis (RDA) was used to model the effect of environmental variables on entire microbial communities, with forw ar d selection used to select statistically significant variables. Significance of RDA models and explanatory v ariables wer e determined using ANOVAs. Finall y, Spearman r ank corr elations wer e emplo y ed to test associations betw een environmental parameters on dominant phyla ( > 1% relative abundance) and ASVs (50 most abundant), with significant correlations visualized using correlation heatmaps.

Distance-decay patterns
Acr oss estuaries, bacterial comm unities fr om sea water displa yed the strongest distance-decay relationship, with Bray-Curtis similarity decreasing with increasing geographic distance ( R = −0.69, P < 0.01; Fig. 2 A). Compar ativ el y weaker r elationships wer e observed for sediment ( R = −0.32, P < 0.01; Fig. 2 C) and fish communities ( R = −0.39, P < 0.01; Fig. 2 E). Bacterial communities associated with the seawater and sediments exhibited stronger associations between community similarity and geographic distance over smaller spatial scales (within estuaries), and this was strongest for seawater communities ( R = −0.71, P < 0.01; Fig. 2 B), follo w ed b y sediments ( R = −0.5, P < 0.01; Fig. 2 D). Conv ersel y, the distancedecay relationship for fish hindgut samples was slightly weaker within estuaries compared to across estuaries ( R = −0.37, P < 0.01; Fig. 2 F). Multiple linear r egr essions r e v ealed significant associations between seawater community similarity and both geogr a phic distance and environmental variation, as well as a significant interaction between distance and environment across ( P < 0.01) and within estuaries ( P < 0.01; Supplementary Table S4). Sediment bacterial communities had significant associations with geogr a phic distance both across and within estuaries; ho w e v er, envir onmental v ariables wer e onl y significant at a lar ger spatial scale ( P < 0.01; Supplementary Table S4). There w as, ho w e v er, significant interactions between environmental variables and geogr a phic distance at both spatial scales. At both spatial scales, fish hindgut bacterial similarity had a weak association with geogr a phic distance ( P < 0.01), but not environmental variables ( P > 0.05); ho w e v er, ther e wer e significant inter actions between geogr a phic distance and envir onmental v ariables (Supplementary  Table S4).

Bacterial community diversity
Comm unity v ariance between estuaries was not equal for bacterial communities associated with P. sexlineatus hosts (PERMDISP; F 5, 57 = 4.846, P = 0.001), seawater ( F 4, 47 = 19.844, P = 0.001), or sediments ( F 5, 75 = 5.7206, P = 0.001). Despite this, the results from the PERMANOVA tests and the nMDS plots indicate that for all sample types, bacterial community composition demonstr ated significant separ ation by estuary ( P < 0.001; Supplementary Table S5), with distinct communities found in each estuary for all sample types ( P < 0.01; Supplementary Table S6) ( Fig. 3 A, C, and D). Similarl y, alpha div ersity (observ ed ric hness and Shannon's div ersity) differ ed significantl y between estuaries for eac h sample type (ANOVA, P < 0.01; Supplementary Table S7) (Fig. 3 B, D, and F). Seawater comm unities demonstr ated an ov er all tr end of decreasing alpha diversity with decreasing latitude ( Figure 3B; Supplementary Table S8). Significant relationships were identified betw een seaw ater bacterial alpha div ersity and geogr a phic distance (Mantel test, R = 0.489, P < 0.001), but not environmental factors ( R = −0.029, P = 0.639). Spearman rank correlations were emplo y ed to investigate associations between individual environmental variables and seawater bacterial alpha diversity measures, with pH, sediment silt, latitude, av er a ge estuary depth, flushing time, catc hment ar ea, ar ea of saltmarsh and mangr ov e in the estuary, and estuary surface area most strongly associated with alpha diversity measures (Supplementary Table S9).
For sediments, observed richness and Shannon diversity were v ariable acr oss estuaries (Figur e 3D; Supplementary Table S8). While significant relationships were not identified between sediment bacterial alpha diversity and geogr a phic distance ( R = 0.002, P = 0.442) or environmental variables ( R = 0.077, P = 0.085), Spearman rank correlations identified significant associations between sediment bacterial alpha diversity measures and particular envir onmental v ariables, specificall y salinity, pH, sediment or ganic matter, estuary volume, catchment size, urbanization percentage, and area of seagrass and mangroves (Supplementary Table S9).
For fish hindguts, bacterial alpha diversity generally increased with decreasing latitude ( Figure 3F; Supplementary Table S8). Significant r elationships wer e identified between fish hindgut bacterial alpha diversity and geographic distance ( R = 0.103, P = 0.01), but not environmental variables ( R = 0.038, P = 0.280). Significant associations between individual environmental variables and fish hindgut bacterial alpha diversity were identified, particularly sediment silt percentage , latitude , estuary flushing time , catchment ar ea, urbanization percenta ge, and ar ea of mangr ov es (Supplementary Table S9).

Relationships with environmental variables
Se v er al envir onmental v ariables wer e identified as significant drivers of variation in seawater and sediment microbial community composition (Figure 4; Supplementary Tables S10 and S11).
Both water column and sediment parameters, along with estuary catc hment par ameters, wer e important for fr ee-living communities, explaining 50.72% of total seawater community variation (Fig. 4 A) and 20.39% of total sediment community variation ( Figure 4B; Supplementary Table S11). This included temperature, salinity, pH, silt, estuary depth, flushing time, and percentage of the catc hment i.e. clear ed and urbanized (Fig. 4 B; Supplementary Table S11). In comparison to seawater and sediment communities , fewer en vironmental variables were identified as significant driv ers of v ariation in P. sexlineatus hindgut micr obial comm unities, with water column pH, catchment area, percentage of urbanization in the estuary catchment, and estuary volume identified as driving 21.66% of the community variation (Figure 4 C; Supplementary Table S11).

Discussion
Unv eiling the r elativ e importance of spatial and environmental factors in the biogeogr a phic distribution and assembly of microbial communities is a central issue in microbial ecology (Chen et al. 2017, Mo et al. 2018. Studies have focused primarily on freeliving communities, but the importance of biogeographic theories for k e y estuarine host-associated comm unities ar e less clear. Her e, we demonstr ate that while estuarine bacterial comm unities associated with sea water, sediments , and the hindgut microbiome of a common estuarine fish are shaped across geographic distance, their distributions ar e sha ped by different factors. At the intermediate scale studied here ( ∼500 km), estuary seawater comm unities ar e str ongl y sha ped by both geogr a phic distance and environmental and catchment variables. Despite similarly weak distance-decay relationships for communities associated with the sediments and fish hindguts, sediment communities were influenced by environmental parameters and characterized by greater species ric hness, potentiall y r eflecting ada ptation to steep biogeoc hemical, and envir onmental gr adients within estuary sediments. On the other hand, the measur ed envir onmental v ariables had minimal influence on ov er all composition of the fish hindgut microbiome, indicating the importance of host-related factors in maintaining structure of the gut microbiome.

Geographical distance influences free-living community dissimilarity
Distance-decay relationships for free-living communities are expected to be weak in open ocean systems due to physical mixing resulting in higher dispersal potential and habitat homogeneity (Zinger et al. 2014. Ho w e v er, coastal envir onments experience m uc h lar ger c hanges in envir onmental v ariation than open ocean environments (Zinger et al. 2014 ), and estuaries act as habitat islands with limited mixing between systems and strong dispersal limitation for bacterial communities acr oss m ultiple systems (Clark et al. 2021 ). Her e, we demonstr ate str ong distance-decay r elationships for seawater bacterial communities at both intermediate and small spatial scales (i.e. across and within estuaries). At small spatial scales, these patterns may reflect the influence of the increased environmental heterogeneity of coastal environments on bacterial community variation.
T he en vir onmental heter ogeneity within estuaries is potentiall y driv en by tidal and fr eshwater exc hanges, and ther efor e water quality variables may be less variable for sediments than seawater. Ne v ertheless, sediment bacterial comm unities ar e likel y to be influenced by limited dispersal potential (sessile lifestyles resulting in spatial isolation) and steep biogeochemical gradients within coastal sediments, potentially resulting in strong habitat heterogeneity (Zinger et al. 2014 ). The sediment communities sampled here display ed w eak distance-decay relationships across estuaries, although this relationship became stronger over smaller spatial scales (within estuaries). There are three main processes that could influence these patterns: First, some micr oor ganisms may be dispersal-limited within sediments, which can lead to a decrease in community similarity, as confirmed by a weak but significant decline trend of similarity between sites with incr easing geogr a phic distance at both spatial scales (Martiny et al. 2011 , Albright andMartiny 2018 ). Dispersal limitation may also allow for ecological drift of bacterial community composition through stochastic births and deaths and limited dispersal potential, increasing patchiness and partially explaining the stronger distance-decay relationship at a smaller spatial scale (Tuomisto et al. 2003, Martiny et al. 2011. Relic DNA (extracellular DNA fr om dead micr oor ganisms that may persist in sediments) can also affect community composition and richness, and given that relic DNA is not subject to en vironmental selection, ma y reduce distance-decay relationships (Lennon et al. 2018, Clark et al. 2021. Finally, species sorting (adaptation to local environments) may also lead to a decrease in community similarity, as evidenced by significant associations with environmental variables at larger spatial scales (i.e. across estuaries) (Hanson et al. 2012 ). Environmental selection has been identified as a major process leading to distance-decay relationships and may explain some of the patterns observed here (Hanson et al. 2012 ).
Another pattern i.e. commonly observed in macroorganisms but still debated in micr oor ganisms is the decline in species richness with increasing latitude (Hillebrand et al. 2004 ). This trend is stronger across regional scales in comparison to local scales, and increases with organism size and trophic level (Hillebrand et al. 2004 ). While seawater bacterial communities sho w ed this predicted pattern of higher diversity at lo w er latitudes, fish hindgut comm unities demonstr ated a weak tr end in the opposite dir ection, and no patterns were detected in sediment communities. The lack of consistent relationships between species richness and latitude here may be a reflection of the relatively small spatial area studied in comparison to previous work that considers m uc h lar ger latitudinal and envir onmental gr adients (i.e. ca pturing both temperate and tropical environments) (Hillebrand et al. 2004, Fuhrman et al. 2008. We also note that species richness differed between sample types, lowest in fish hindgut microbiome samples, and greatest in sediments. This may be a reflection of the specialized role of the gut microbiota (Roeselers et al. 2011 ) and the vast biogeochemical cycles occurring in sediments (Hicks et al. 2018 ). Higher species richness in sediments may be attributed to steep biogeoc hemical gr adients, higher nutrient concentrations, and higher habitat heterogeneity (Zhao et al. 2020 ). This increased species richness may also reflect the ability of sediment bacteria to respond to changes in environmental conditions , making this en vir onment mor e r esistant to envir onmental c hange (K erfahi et al. 2014 ), and supporting the idea that sediment comm unities studied her e ar e mor e str ongl y driv en by envir onmental selection than dispersal limitation. It is important to note that while the Shannon diversity index provides more inferences about community composition and is more robust than simple species richness or evenness by also taking relative abundances of different species into consideration (Kim et al. 2017, Roswell et al. 2021, total microbial diversity was not circumscribed, and thus diversity estimates may not capture all members of the community.

Environmental factors dri v e variation in free-li ving comm unities
Temper atur e, salinity, and nutrients have been identified as significant drivers of variation in free-living aquatic microbial communities (Fuhrman et al. 2008, Wietz et al. 2010, Zhang et al. 2022. At the intermediate scale studied here ( ∼500 km), we provide evidence that water quality and estuary catchment par ameters ar e important driv ers for estuary seawater bacterial comm unities. Specificall y, water column pH and salinity, as well as av er a ge depth, flushing time, and percentage of the catchment i.e. clear ed, wer e significant driv ers for seawater bacterial comm unity composition and div ersity. Gener a within the Cyanobacteria, in particular ASVs identified as Synechococcus CC9902 and Cyanobium PCC-6307 , were associated with increased pH, decreased salinity, and smaller catchment areas with decreased saltmarsh and mangr ov e ar eas. These gener a ar e important primary producers in open ocean environments and have been fr equentl y associated with blooms in coastal ar eas, particularl y after storms or large freshwater inputs (Xia et al. 2015, Li et al. 2019. T hey ha ve also been associated with other abundant taxa r eported her e, including Candidatus Actinomarina, HIMB11 , Winogradskyella , and NS5 marine gr oup, whic h wer e also found associated with similar envir onmental v ariables (Li et al. 2019, Fortin et al. 2022. For sediment communities, significant associations between community similarity and environmental variables were only documented at larger spatial scales, indicating that these communities may be less strongly influenced by dispersal than envir onmental filtering acr oss lar ger spatial scales. Her e, salinity was most fr equentl y associated with sediment microbial comm unity div ersity and composition, consistent with findings from other systems (Webster et al. 2015, Vekeman et al. 2016, Huang et al. 2019, Yue et al. 2022. The most abundant ASVs belonging to taxa within the Proteobacteria ( Methyloceanibacter and Woeseia ) and Actinobacteria (Propionibacteriaceae and Actinomarinales) wer e significantl y corr elated with salinity and are abundant in estuarine sediments, likely carrying out diverse ecological functions including denitrification, sulfur oxidation, and aerobic ammonium oxidation (Vekeman et al. 2016, Mußmann et al. 2017, Rios-Del Toro et al. 2018, Zhang et al. 2020. Pr e vious findings also indicate, ho w e v er, that ther e is a large amount of unexplained variation in these communities, consistent with findings here . T hese studies suggest that this is a result of either unmeasur ed envir onmental v ariability or stoc hastic pr ocesses suc h as ecological drift or random speciation and extinction (Martiny et al. 2011, Xiong et al. 2014, Yue et al. 2022, Zhang et al. 2022 ).

Pela tes sexlinea tus hindgut microbiome is go verned b y host control
Pr e vious work on fish has r e v ealed that both environmental and host-r elated factors ar e important in sha ping the hindgut microbiome (Sullam et al. 2012, Stephens et al. 2016, Tarnecki et al. 2017. Studies investigating the influence of spatial factors on the fish gut microbiome are limited; ho w ever, one study found that community composition was not significantly impacted by geogr a phy and instead host-related factors, particularly life stage, str ongl y defined community composition, and diversity (Llewellyn et al. 2016 ). While alpha div ersity gener all y incr eased with decreasing latitude , distance-deca y r elationships wer e weak acr oss both spatial scales examined here, and associations with envir onmental v ariables wer e gener all y limited and weak, suggesting that host-r elated factors, r ather than spatial or environmental factors , pla y a primary role in structuring and maintaining P. sexlineatus hindgut bacterial comm unities. Pr e vious work has also highlighted the importance of host-related factors rather than the external environment in driving shifts in the gut microbiome of fish, e.g. host phylogeny (Sullam et al. 2012 ) and gut physiology (Stephens et al. 2016 ). Host genetics may also influence the gut microbiome (Kokou et al. 2018 ), and genetic structure of dispersal-limited organisms has been shown to correlate with geogr a phy (e.g. algae) (Wood et al. 2022 ). Pelates sexlineatus is thought to spawn near the mouth of estuaries, with juveniles moving into and remaining in estuaries for at least one year after settlement; ther efor e , dispersal ma y be limited to an individual estuary (Smith and Suthers 2000 ), and may explain the weaker distance-decay relationships on a smaller spatial scale (within estuaries). It is important to note, ho w e v er, that we did not measure the age of individuals nor the internal environmental conditions of the fish hindgut, and these may have important driving forces on these communities , limiting our ability to draw conclusions on the buffering effect of the host. Future work should consider the effect of both the external and internal environment on the fish gut microbiome in order to further disentangle the responses of these communities to spatial and environmental influences.
While many of the dominant ASVs reported here are common marine and estuarine environmental taxa (e.g. Methyloceaniabcter , Cyanobium PCC-6307 , and Synechococcus CC9902 ), ASVs belonging to the bacterial families Pr opionibacteriaceae, Rhodobacter aceae, and Vibrionaceae were also abundant, consistent with previous findings (Larios-Soriano et al. 2021. The Propionibacteriaceae produce microbial metabolites during glucose fermentation and enzymes for fatty acid degradation (Neis et al. 2015, Cha pa gain et al. 2019 ) that may help in the breakdown of food, pr oduce v aluable nutrients, and ener gy. Relationships between abundance of the Propionibacteriaceae and host diet have been established (Larios-Soriano et al. 2021 ), and given that P. sexlineatus is an opportunistic carnivore that feeds on dominant food sources within sea gr ass meadows , en vironmental filtering may play an important role in structuring and maintaining the gut micr obiome acr oss estuaries thr ough diet; ho w e v er, this was not measur ed her e (Sanc hez-Jer ez et al. 2002, Loo et al. 2019 ).

Conclusion
A central goal in microbial ecology is understanding the contribution of spatial and environmental factors in driving patterns of microbial composition and diversity. Biogeography has been shown to a ppl y to fr ee-living micr obial comm unities acr oss in-terconnected aquatic en vironments , with the relative importance of specific environmental variables identified at a range of spatial scales. Patterns in free-living bacterial communities are likely to differ from those in host-associated comm unities, whic h may be buffered from environmental variation and under stronger selectiv e pr essur e fr om the nic he habitat pr ovided by the host and ar e compar ativ el y less well understood. We found that spatial and environmental factors have different influences on the bacterial communities associated with estuary sea water, sediments , and a common estuarine fish, P. sexlineatus , across six eastern Australian estuaries spanning ∼500 km. Seawater communities exhibited strong distance-decay relationships as well as significant associations with a range of en vironmental variables . Conv ersel y, sediment and P. sexlineatus hindgut bacterial communities displayed weak distance-decay relationships and limited variation explained by measured environmental variables, potentially r eflecting envir onmental filtering acr oss biogeoc hemical gr adients or stoc hastic pr ocesses within estuary sediments and that host-associated comm unities ar e gov erned most str ongl y by hostrelated factors . T hese r esults pr ovide important ecological insights into the spatial distribution and some of the driving factors of bacterial community composition across temperate estuarine systems for multiple sample types.

Ac kno wledgements
The authors would like to thank Molly Grew, Harrison Smith, Finla y J ohnson, Mark Bennett, Ben Cuerel, and James Wong for their assistance in the field. We would also like to acknowledge the contribution of the Austr alian Micr obiome Consortium in the generation of data used in this publication.

Supplementary data
Supplementary data are available at FEMSEC online.

Conflict of interest.
The authors declare no conflicts of interest.

Funding
The Australian Microbiome Initiative is supported by funding fr om Bioplatforms Austr alia and the Integr ated Marine Observing System (IMOS) through the Austr alian Gov ernment's National Collabor ativ e Researc h Infr astructur e Str ategy (NCRIS), P arks Austr alia thr ough the Bush Blitz pr ogr am funded by the Austr alian Gov ernment and BHP, and the CSIRO.

Da ta av ailability
The sequence data from seawater and sediment samples are fr eel y av ailable at the BioPlatforms Austr alia data portal under the Australian Microbiome project (DOI: 102.100/401 931)